{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T14:40:12Z","timestamp":1769352012859,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2025R330"],"award-info":[{"award-number":["PNURSP2025R330"]}]},{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2025R330"],"award-info":[{"award-number":["PNURSP2025R330"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00873-w","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T12:04:16Z","timestamp":1748865856000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Enhanced Wheat Stripe Rust Segmentation Approach Using Vision Transformer Model"],"prefix":"10.1007","volume":"18","author":[{"given":"Nosheen","family":"Usman","sequence":"first","affiliation":[]},{"given":"Tauqir","family":"Ahmad","sequence":"additional","affiliation":[]},{"given":"Faiza","family":"Iqbal","sequence":"additional","affiliation":[]},{"given":"Ayesha","family":"Altaf","sequence":"additional","affiliation":[]},{"given":"Nagwan Abdel","family":"Samee","sequence":"additional","affiliation":[]},{"given":"Manal Abdullah","family":"Alohali","sequence":"additional","affiliation":[]},{"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"873_CR1","unstructured":"Johancontributors: Agriculture in Pakistan (2025). https:\/\/en.wikipedia.org\/wiki\/Agriculture_in_Pakistan#Crops"},{"key":"873_CR2","unstructured":"Eliajoe: World-Grain (2017). https:\/\/www.world-grain.com\/articles\/10791-pakistan-wheat-production-up-in-2017-18"},{"key":"873_CR3","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.techfore.2017.02.012","volume":"118","author":"M Ray","year":"2017","unstructured":"Ray, M., Rai, A., Singh, K., Ramasubramanian, V., Kumar, A.: Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India. Technol. Forecast. Soc. Change 118, 128\u2013133 (2017)","journal-title":"Technol. Forecast. Soc. Change"},{"key":"873_CR4","unstructured":"Neck: American Society of Agronomy (2022). https:\/\/en.wikipedia.org\/wiki\/American_Society_of_Agronomy"},{"key":"873_CR5","unstructured":"Abhishek, A.: Agriculture review: learn agriculture and home gardening (2025). https:\/\/agriculturereview.com\/"},{"key":"873_CR6","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.compag.2018.10.017","volume":"155","author":"J Su","year":"2018","unstructured":"Su, J., Liu, C., Coombes, M., Hu, X., Wang, C., Xu, X., Li, Q., Guo, L., Chen, W.-H.: Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Comput. Electron. Agric. 155, 157\u2013166 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"873_CR7","unstructured":"trudi: Wheat | diseases and pests, description, uses, Propagation (2022). https:\/\/plantvillage.psu.edu\/topics\/wheat\/infos"},{"key":"873_CR8","unstructured":"Allea: Integrated pest and crop management (2021). https:\/\/ipcm.wisc.edu\/"},{"key":"873_CR9","doi-asserted-by":"crossref","unstructured":"Pryzant, R., Ermon, S., Lobell, D.: Monitoring Ethiopian wheat fungus with satellite imagery and deep feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 39\u201347 (2017)","DOI":"10.1109\/CVPRW.2017.196"},{"key":"873_CR10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.4108\/eetiot.5325","volume":"10","author":"M Mandava","year":"2024","unstructured":"Mandava, M., Vinta, S.R., Ghosh, H., Rahat, I.S.: Identification and categorization of yellow rust infection in wheat through deep learning techniques. EAI Endorsed Trans. Internet of Things 10, 121 (2024)","journal-title":"EAI Endorsed Trans. Internet of Things"},{"key":"873_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.microc.2023.109790","volume":"197","author":"HC Reis","year":"2024","unstructured":"Reis, H.C., Turk, V.: Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection. Microchem. J. 197, 109790 (2024)","journal-title":"Microchem. J."},{"issue":"15","key":"873_CR12","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.3390\/plants12152814","volume":"12","author":"Q Liu","year":"2023","unstructured":"Liu, Q., Sun, T., Wen, X., Zeng, M., Chen, J.: Detecting the minimum limit on wheat stripe rust in the latent period using proximal remote sensing coupled with duplex real-time pcr and machine learning. Plants 12(15), 2814 (2023)","journal-title":"Plants"},{"issue":"17","key":"873_CR13","doi-asserted-by":"publisher","first-page":"9987","DOI":"10.3390\/app13179987","volume":"13","author":"Z Li","year":"2023","unstructured":"Li, Z., Fang, X., Zhen, T., Zhu, Y.: Detection of wheat yellow rust disease severity based on improved GhostNetV2. Appl. Sci. 13(17), 9987 (2023)","journal-title":"Appl. Sci."},{"key":"873_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100305","volume":"8","author":"R Mumtaz","year":"2023","unstructured":"Mumtaz, R., Maqsood, M.H., Haq, I., Shafi, U., Mahmood, Z., Mumtaz, M.: Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading. Decis. Anal. J. 8, 100305 (2023)","journal-title":"Decis. Anal. J."},{"key":"873_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107211","volume":"200","author":"J Deng","year":"2022","unstructured":"Deng, J., Zhou, H., Lv, X., Yang, L., Shang, J., Sun, Q., Zheng, X., Zhou, C., Zhao, B., Wu, J., et al.: Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using rgb-based high spatial resolution images from uavs. Comput. Electron. Agric. 200, 107211 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"873_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107709","volume":"207","author":"Z Tang","year":"2023","unstructured":"Tang, Z., Wang, M., Schirrmann, M., Dammer, K.-H., Li, X., Brueggeman, R., Sankaran, S., Carter, A.H., Pumphrey, M.O., Hu, Y., et al.: Affordable high throughput field detection of wheat stripe rust using deep learning with semi-automated image labeling. Comput. Electron. Agric. 207, 107709 (2023)","journal-title":"Comput. Electron. Agric."},{"issue":"9","key":"873_CR17","doi-asserted-by":"publisher","first-page":"9057","DOI":"10.1109\/JSEN.2022.3156097","volume":"22","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Yang, Z., Xu, Z., Li, J.: Wheat yellow rust severity detection by efficient DF-UNet and UAV multispectral imagery. IEEE Sens. J. 22(9), 9057\u20139068 (2022)","journal-title":"IEEE Sens. J."},{"key":"873_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.876069","volume":"13","author":"H Liu","year":"2022","unstructured":"Liu, H., Jiao, L., Wang, R., Xie, C., Du, J., Chen, H., Li, R.: WSRD-Net: a convolutional neural network-based arbitrary-oriented wheat stripe rust detection method. Front. Plant Sci. 13, 876069 (2022)","journal-title":"Front. Plant Sci."},{"issue":"19","key":"873_CR19","doi-asserted-by":"publisher","first-page":"3892","DOI":"10.3390\/rs13193892","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang, T., Xu, Z., Su, J., Yang, Z., Liu, C., Chen, W.-H., Li, J.: IR-UNet: irregular segmentation u-shape network for wheat yellow rust detection by UAV multispectral imagery. Remote Sens. 13(19), 3892 (2021)","journal-title":"Remote Sens."},{"key":"873_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2021.469689","volume":"12","author":"M Schirrmann","year":"2021","unstructured":"Schirrmann, M., Landwehr, N., Giebel, A., Garz, A., Dammer, K.-H.: Early detection of stripe rust in winter wheat using deep residual neural networks. Front. Plant Sci. 12, 469689 (2021)","journal-title":"Front. Plant Sci."},{"issue":"1","key":"873_CR21","doi-asserted-by":"publisher","first-page":"146","DOI":"10.3390\/s22010146","volume":"22","author":"U Shafi","year":"2021","unstructured":"Shafi, U., Mumtaz, R., Haq, I.U., Hafeez, M., Iqbal, N., Shaukat, A., Zaidi, S.M.H., Mahmood, Z.: Wheat yellow rust disease infection type classification using texture features. Sensors 22(1), 146 (2021)","journal-title":"Sensors"},{"issue":"3","key":"873_CR22","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1007\/s42161-021-00886-2","volume":"103","author":"T Hayit","year":"2021","unstructured":"Hayit, T., Erbay, H., Var\u00e7\u0131n, F., Hayit, F., Akci, N.: Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. J. Plant Pathol. 103(3), 923\u2013934 (2021)","journal-title":"J. Plant Pathol."},{"key":"873_CR23","doi-asserted-by":"publisher","first-page":"23726","DOI":"10.1109\/ACCESS.2023.3254430","volume":"11","author":"U Shafi","year":"2023","unstructured":"Shafi, U., Mumtaz, R., Qureshi, M.D.M., Mahmood, Z., Tanveer, S.K., Haq, I.U., Zaidi, S.M.H.: Embedded AI for wheat yellow rust infection type classification. IEEE Access 11, 23726\u201323738 (2023)","journal-title":"IEEE Access"},{"issue":"19","key":"873_CR24","doi-asserted-by":"publisher","first-page":"6540","DOI":"10.3390\/s21196540","volume":"21","author":"Q Pan","year":"2021","unstructured":"Pan, Q., Gao, M., Wu, P., Yan, J., Li, S.: A deep-learning-based approach for wheat yellow rust disease recognition from unmanned aerial vehicle images. Sensors 21(19), 6540 (2021)","journal-title":"Sensors"},{"key":"873_CR25","doi-asserted-by":"crossref","unstructured":"Kumar, D., Kukreja, V.: An instance segmentation approach for wheat yellow rust disease recognition. In: 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 926\u2013931. IEEE (2021)","DOI":"10.1109\/DASA53625.2021.9682257"},{"issue":"11","key":"873_CR26","doi-asserted-by":"publisher","first-page":"468","DOI":"10.3390\/plants8110468","volume":"8","author":"MH Saleem","year":"2019","unstructured":"Saleem, M.H., Potgieter, J., Arif, K.M.: Plant disease detection and classification by deep learning. Plants 8(11), 468 (2019)","journal-title":"Plants"},{"key":"873_CR27","doi-asserted-by":"crossref","unstructured":"Li, H., Li, S., Yu, J., Han, Y., Dong, A.: Plant disease and insect pest identification based on vision transformer. In: International Conference on Internet of Things and Machine Learning (IoTML 2021), vol. 12174, pp. 194\u2013201. SPIE (2022)","DOI":"10.1117\/12.2628467"},{"issue":"2","key":"873_CR28","doi-asserted-by":"publisher","first-page":"72","DOI":"10.54097\/fcis.v4i2.10209","volume":"4","author":"J Zhang","year":"2023","unstructured":"Zhang, J.: Weed recognition method based on hybrid CNN-transformer model. Front. Comput. Intell. Syst. 4(2), 72\u201377 (2023)","journal-title":"Front. Comput. Intell. Syst."},{"issue":"6","key":"873_CR29","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.3390\/rs14061400","volume":"14","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Han, L., Sobeih, T., Lappin, L., Lee, M.A., Howard, A., Kisdi, A.: The self-supervised spectral-spatial vision transformer network for accurate prediction of wheat nitrogen status from UAV imagery. Remote Sens. 14(6), 1400 (2022)","journal-title":"Remote Sens."},{"issue":"2","key":"873_CR30","first-page":"249","volume":"11","author":"X Fu","year":"2024","unstructured":"Fu, X., Ma, Q., Yang, F., Zhang, C., Zhao, X., Chang, F., Han, L.: Crop pest image recognition based on the improved ViT method. Inf. Process. Agric. 11(2), 249\u2013259 (2024)","journal-title":"Inf. Process. Agric."},{"key":"873_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124833","volume":"255","author":"M Fang","year":"2024","unstructured":"Fang, M., Tan, Z., Tang, Y., Chen, W., Huang, H., Dananjayan, S., He, Y., Luo, S.: Pest-conformer: a hybrid CNN-transformer architecture for large-scale multi-class crop pest recognition. Expert Syste. Appl. 255, 124833 (2024)","journal-title":"Expert Syste. Appl."},{"key":"873_CR32","doi-asserted-by":"crossref","unstructured":"Thakur, P.S., Khanna, P., Sheorey, T., Ojha, A.: Vision transformer for plant disease detection: PlantViT. In: International Conference on Computer Vision and Image Processing, pp. 501\u2013511. Springer (2021)","DOI":"10.1007\/978-3-031-11346-8_43"},{"issue":"1","key":"873_CR33","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3390\/agriculture12010074","volume":"12","author":"L Huang","year":"2022","unstructured":"Huang, L., Liu, Y., Huang, W., Dong, Y., Ma, H., Wu, K., Guo, A.: Combining random forest and XGBoost methods in detecting early and mid-term winter wheat stripe rust using canopy level hyperspectral measurements. Agriculture 12(1), 74 (2022)","journal-title":"Agriculture"},{"issue":"29","key":"873_CR34","doi-asserted-by":"publisher","first-page":"72221","DOI":"10.1007\/s11042-024-18463-x","volume":"83","author":"D Kumar","year":"2024","unstructured":"Kumar, D., Kukreja, V., Singh, A.: A novel hybrid segmentation technique for identification of wheat rust diseases. Multimedia Tools Appl. 83(29), 72221\u201372251 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"873_CR35","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.aej.2024.09.018","volume":"107","author":"A Hassan","year":"2024","unstructured":"Hassan, A., Mumtaz, R., Mahmood, Z., Fayyaz, M., Naeem, M.K.: Wheat leaf localization and segmentation for yellow rust disease detection in complex natural backgrounds. Alex. Eng. J. 107, 786\u2013798 (2024)","journal-title":"Alex. Eng. J."},{"key":"873_CR36","unstructured":"Nermena: Wheat disease detection (2020). https:\/\/www.kaggle.com\/datasets\/sinadunk23\/behzad-safari-jalal"},{"key":"873_CR37","unstructured":"Zia: YELLOW-RUST-19 (2021). https:\/\/www.kaggle.com\/datasets\/tolgahayit\/yellowrust19-yellow-rust-disease-in-wheat"},{"issue":"2\u20133","key":"873_CR38","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/S0034-4257(01)00342-X","volume":"81","author":"E Boegh","year":"2002","unstructured":"Boegh, E., Soegaard, H., Broge, N., Hasager, C., Jensen, N., Schelde, K., Thomsen, A.: Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ. 81(2\u20133), 179\u2013193 (2002)","journal-title":"Remote Sens. Environ."},{"issue":"10s","key":"873_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (CSUR) 54(10s), 1\u201341 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"873_CR40","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: transformers for image recognition at scale. 59, 76 (2020). arXiv:2010.11929"},{"key":"873_CR41","first-page":"49","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141, Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 49\u201355 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"873_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2022.103664","volume":"89","author":"K Jiang","year":"2022","unstructured":"Jiang, K., Peng, P., Lian, Y., Xu, W.: The encoding method of position embeddings in vision transformer. J. Vis. Commun. Image Represent. 89, 103664 (2022)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"9","key":"873_CR43","doi-asserted-by":"publisher","first-page":"5801","DOI":"10.3390\/app13095801","volume":"13","author":"X Fang","year":"2023","unstructured":"Fang, X., Zhen, T., Li, Z.: Lightweight multiscale CNN model for wheat disease detection. Appl. Sci. 13(9), 5801 (2023)","journal-title":"Appl. Sci."},{"key":"873_CR44","doi-asserted-by":"crossref","unstructured":"Mishra, K., Kaur, K.: Advanced image processing and feature extraction approaches for detecting wheat yellow rust disease. In: 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC), pp. 1\u20137. IEEE (2024)","DOI":"10.1109\/ICICEC62498.2024.10808354"},{"key":"873_CR45","doi-asserted-by":"crossref","unstructured":"Mishra, K., Kaur, K.: Improving wheat yield protection with deep feature fusion for estimating yellow rust severity using CNN models. In: 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 593\u2013598. IEEE (2024)","DOI":"10.1109\/ICSSAS64001.2024.10760429"},{"key":"873_CR46","doi-asserted-by":"crossref","unstructured":"Mishra, K., Kaur, K.: A comprehensive analysis of deep learning techniques for wheat yellow rust disease detection and classification. In: 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 1279\u20131284. IEEE (2025)","DOI":"10.1109\/IDCIOT64235.2025.10915123"},{"key":"873_CR47","doi-asserted-by":"crossref","unstructured":"Ahsan, A., Iqbal, M.S., Ahmar, M., Adnan, M., Akbar, M.A., Bermak, A.: Edge computing based early yellow rust disease detection in wheat plants. In: 2024 International Conference on Microelectronics (ICM), pp. 1\u20136. IEEE (2024)","DOI":"10.1109\/ICM63406.2024.10815846"},{"key":"873_CR48","doi-asserted-by":"publisher","first-page":"4126","DOI":"10.1111\/pce.15413","volume":"8","author":"F Syeda","year":"2025","unstructured":"Syeda, F., Jameel, A., Alani, N., Humayun, M., Alwakid, G.N.: Automated detection and severity prediction of wheat rust using cost-effective Xception architecture. Plant Cell Environ. 8, 4126\u20134139 (2025)","journal-title":"Plant Cell Environ."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00873-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00873-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00873-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T12:04:25Z","timestamp":1748865865000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00873-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["873"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00873-w","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]},"assertion":[{"value":"12 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"137"}}